Fish and health with national fish promotion programme
This is an open assessment looking at potential health effects of a national fish promotion program in Finland. The details of the assessment are described at Opasnet. This file contains the R code to run the assessment model.
Knit to html for best performance.
Calculation is based on BONUS GOHERR project and its http://en.opasnet.org/w/Goherr_assessment.
What needs to be done for PFAS assessment?
- Amount should be gender and age-specific, because we need to worry about young mothers. Solution: take KKE amounts of fish, and scale those with the Goherr subgroup-specific proportions. DONE
- Amounts should reflect the actual fish consumption in Porvoo. Solution: postpone and use national statistics. DONE
- Infant’s dioxin concentration module must be added. Solution: Use Goherr model for expo_indir DONE
- The module must be updated to match PFAS as well. Solution: update the module to contain column Exposure_agent. Change body fat parameter to volume of distribution.
- Find why expo_indir is so much higher than EFSA toxicokinetic assessment. Make an alternative model.
- Add ERF for PFAS (sum of PFOS, PFHxS, PFOA, PFNA). Solution: Make a new page for ERF of PFAS and add that to adjusted ERFs. First immunology; postpone cholesterol and low birth weight and liver toxicity. DONE
- Add case burdens for PFAS outcomes. Solution: give rough estimates for immunology to get started. DONE
- Add PFAS concentrations to data. Solution: Look at Porvoo concentrations first and make conc_pfas; combine that with conc_vit. DONE
- Add dioxin and MeHg concentrations. Later.
- Now that the model runs technically, look through each part to check that it makes sense.
- If PAF contains some inputs with Age and some without Age (implying that they apply to any age group), only the explicit age groups remain in the end. Add RRorig to compensate for this.
objects.latest("Op_en2261", code_name="RR2") # RR on page [[Health impact assessment]]## Loading objects:
## RRRRorig <- RR@formula
RR@formula <- function(...) {
out <- RRorig()
out <- out * Ovariable(output=data.frame(Age=c(
"0 - 4", "5 - 9", "10 - 14", "15 - 19", "20 - 24", "25 - 29", "30 - 34", "35 - 39", "40 - 44", "45 - 49", "50 - 54",
"55 - 59", "60 - 64", "65 - 69", "70 - 74", "75 - 79", "80 - 84", "85+"),
Result=1),
marginal=c(TRUE,FALSE)
)
return(out)
}# This code was forked from https://github.com/jtuomist/fishhealth/blob/master/fishhealth.Rmd
# This code was previously forked from code Op_fi5923/model on page [[Kotimaisen kalan edistämisohjelma]]
# The code was even more previously forked from Op_fi5889/model on page [[Ruori]] and Op_en7748/model on page [[Goherr assessment]]
dat <- opbase.data("Op_fi5932", subset="Malliparametrit")[-1] # [[PFAS-yhdeisteiden tautitaakka]]
dec <- opbase.data("Op_fi5932", subset="Decisions")[-1]
DecisionTableParser(dec)
CTable <- opbase.data("Op_fi5932",subset="CollapseMarginals")
#for(i in 1:ncol(CTable)) {CTable[[i]] <- as.character(CTable[[i]])} # The default is currently character, not factor
CollapseTableParser(CTable)
cat("Laskennassa käytetty data.\n")## Laskennassa käytetty data.datcat("Tarkastellut päätökset.\n")## Tarkastellut päätökset.deccat("Aggregoidut marginaalit.\n")## Aggregoidut marginaalit.CTabledummy <- Ovariable("dummy",data=data.frame(Age="dummy", Result=1))
fish_proportion <- Ovariable( # How age groups eat fish differently
"fish_proportion",
dependencies = data.frame(Name="dummy"),
formula = function(...) {
out = prepare(dat,"fish_proportion",c("Type","Exposure_agent","Response","Unit"))
out$Result <- as.numeric(as.character(out$Result))
out$Result <- out$Result / sum(out$Result) * length(out$Result)
return(out)
},
unit="-")
amount <- Ovariable(
"amount",
dependencies = data.frame(Name="fish_proportion"),
formula = function(...) {
amount = Ovariable("total_amount", data=prepare(dat, "amount", c("Type","Response","Exposure_agent","Unit")))
# Filleted weight, i.e. no loss.
amount <- amount * 1000 / 5.52 /365.25
# M kg/a per 5.52M population --> g/d per average person.
amount <- amount * fish_proportion
# fish_proportion tells the relative amount in each subgroup
# Match KKE-classification in amount with Fineli classification
tmp <- Ovariable(
output = data.frame(
Kala = c("Kasvatettu", "Kaupallinen", "Kirjolohi", "Silakka", "Vapaa-ajan", "Muu tuonti", "Tuontikirjolohi", "Tuontilohi"),
Fish = c("Whitefish", "Average fish","Rainbow trout", "Herring", "Average fish", "Average fish", "Rainbow trout", "Salmon"),
Result = 1
),
marginal = c(TRUE, TRUE, FALSE)
)
amount <- amount * tmp
return(amount)
},
unit="g/d"
)
# Exposure:To child and To eater not needed, because dioxins are not (yet) included
amount <- EvalOutput(amount)## Loading required package: reshape2##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
##
## smithsggplot(amount@output, aes(x=Age, weight=amountResult, fill=Kala))+geom_bar()+
labs(
title="Kalansyönti Suomessa ikäryhmittäin",
y="Syönti (g/d)"
)
ggsave("Kalansyönti Suomessa ikäryhmittäin.svg")## Saving 7 x 5 in imagepopulation <- Ovariable(
"population",
data = prepare(dat,"population",c("Type","Exposure_agent","Response","Unit")),
unit="#")
population <- Ovariable(
"population",
data=prepare(dat, "population", c("Type", "Exposure_agent", "Response","Unit")),
unit = "#"
)
incidence <- Ovariable(
"incidence",
data = prepare(dat,"incidence",c("Type","Exposure_agent","Unit")),
unit="1/person-year")
#incidence@data$Age[is.na(incidence@data$Age)] <- ""
case_burden <- Ovariable(
"case_burden",
data = prepare(dat,"case burden",c("Type", "Exposure_agent","Unit")),
unit="DALY/case")
ERFchoice <- Ovariable(
"ERFchoice",
data =
prepare(dat, "ERFchoice", c("Unit", "Type"))
)
InpBoD <- EvalOutput(Ovariable( # Evaluated because is not a dependency but an Input
"InpBoD",
data = prepare(dat, "BoD", c("Type","Exposure_agent","Unit")),
unit="DALY/a"
))
InpBoD$Response[InpBoD$Response=="All causes"] <- "All-cause mortality"
InpBoD$Response[InpBoD$Response=="Depressive disorders"] <- "Depression"
InpBoD$Response[InpBoD$Response=="Neoplasms"] <- "Cancer morbidity"
InpBoD$Response[InpBoD$Response=="Respiratory infections and tuberculosis"] <- "Immunosuppression" # Infections of 0-9-year-olds are assumed to represent the background BoD of immunosuppressive effect of PFAS
InpBoD$Response[InpBoD$Response=="Cardiovascular diseases"] <- "CHD2 mortality"conc_vit <- Ovariable(
"conc_vit",
ddata = "Op_en1838", # [[Concentrations of beneficial nutrients in fish]]
subset = "Fineli data for common fish species"
)
df = conc_vit@data
df$Nutrient[df$Nutrient=="D-vitamiini (µg)"] <- "Vitamin D"
df$Nutrient[df$Nutrient=="rasvahapot n-3 moni-tyydyttymättömät (g)"] <- "Omega3"
df$Nutrient[df$Nutrient=="rasvahappo 18:3 n-3 (alfalinoleenihappo) (mg)"] <- "ALA"
df$Nutrient[df$Nutrient=="rasvahappo 22:6 n-3 (DHA) (mg)"] <- "DHA"
df$Nutrient[df$Nutrient=="proteiini (g)"] <- "Fish"
df$conc_vitResult[df$Nutrient=="Fish"] <- "1"
df <- dropall(df[df$Nutrient %in% c("Vitamin D", "Omega3", "ALA", "DHA", "Fish") , ])
conc_vit@data <- df
######## Concentration of PFAS
# Data from EU-kalat3 (Finland excl Vanhankaupunginlahti): # pg/g fresh weight
# POP mean sd min Q0.025 median Q0.975 max
# 2.5% PFOS 2055.757 1404.045 305.0399 330.1365 1533.269 5029.697 5814.935
# Data from EU-kalat3 (Vanhankaupunginlahti, Helsinki) # ng/g f.w.
# POP mean sd min Q0.025 median Q0.975 max
#2.5% PFOS 14.428 11.94542 1.499441 1.607789 15.64988 35.32517 38.91994
conc_eukalat <- EvalOutput(Ovariable(
"conc_eukalat",
data = data.frame(
Area = c("Suomi","Helsinki"),
Compound="PFOS",
Result=c("2.056 (3.301 - 5.030)", "14.428 (1.499 - 35.325)")),
unit="ng/g fresh weight"
))
conc_pfas <- Ovariable(
"conc_pfas",
data=opbase.data("Op_fi5932",subset="PFAS concentrations"),
unit="ng/g fresh weight")
conc_pfas@data$Area <- "Porvoo"
conc_pfas <- EvalOutput(conc_pfas)
ggplot(conc_pfas@output, aes(x=conc_pfasResult, color=Compound, linetype=Area))+stat_ecdf()+
scale_x_log10()+
stat_ecdf(data=conc_eukalat@output, aes(x=conc_eukalatResult))+
scale_linetype_manual(values=c("dotted","solid","twodash"))+
labs(
title="PFAS concentration in fishes in Finland",
x="PFAS concentration (ng/g fresh weight)",
y="Cumulative probability"
)
# The code may produce some negative values, which are removed from the graph
ggsave("PFAS-pitoisuus kalassa Suomessa.svg")## Saving 7 x 5 in imagesum_pfas <- oapply(conc_pfas, cols=c("Kala","Compound"), FUN=sum)
tmp <- conc_pfas / sum_pfas
summary(tmp, marginals="Compound")## This tells that PFOS consists of 71 - 97 % of the four key PFAS, while PFOA, PFNA, and PFHxS consist of
# 0 - 10 %, 2 - 18 %, and 0 - 9 %, respectively.
# Even if we included the next most abundant congeners, i.e. PFDA and PFUnA, the overall picture would not change.
conc <- Ovariable(
"conc",
dependencies = data.frame(Name="conc_vit", "conc_pfas"),
formula = function(...){
conc_vit <- oapply(conc_vit, cols=c("Kala", "Adjust"),FUN=mean)
colnames(conc_vit@output)[colnames(conc_vit@output)=="Nutrient"] <- "Compound"
conc_pfas <- oapply(conc_pfas, cols=c("Obs","Area"), FUN=mean)
conc_pfas$Compound[conc_pfas$Compound %in% c("PFOA","PFNA","PFHxS","PFOS")] <- "PFAS"
conc_pfas <- oapply(conc_pfas, cols="", FUN=sum)
out <- OpasnetUtils::combine(conc_vit, conc_pfas)
return(out)
}
)
conc <- EvalOutput(conc)
ggplot(conc@output, aes(x=concResult, colour=Fish))+stat_ecdf()+
facet_wrap(~Compound, scales="free_x")
###################################################################
# Code copied from http://en.opasnet.org/w/Goherr_assessment#
mc2dparam<- list(
N2 = 10, # Number of iterations in the new Iter
strength = 50, # Sample size to which the fun is to be applied. Resembles number of observations
run2d = FALSE, # Should the mc2d function be used or not?
info = 1, # Ovariable that contains additional indices, e.g. newmarginals.
newmarginals = c("Group","Exposure"), # Names of columns that are non-marginals but should be sampled enough to become marginals
method = "bootstrap", # which method to use for 2D Monte Carlo? Currently bootsrap is the only option.
fun = mean # Function for aggregating the first Iter dimension.
)
if(FALSE) {
## Exposure with background exposure but without mother's exposure to child
expo_dir <- Ovariable(
"expo_dir",
dependencies=data.frame(Name=c("amount","conc","expo_bg")),
formula = function(...) {
out <- conc[conc$Exposure_agent=="TEQ",] * 0 + 1
out$Exposure_agent <- "Fish"
out <- combine(conc, out, name="conc")
out <- oapply(amount * out, cols="Fish", FUN=sum)
out <- Ovariable(output = data.frame(
Exposcen = c("BAU", "No exposure"),
Result = c(1, 0)
), marginal=c(TRUE,FALSE)) * out + expo_bg
out$Exposure <- as.factor(
ifelse(
out$Exposure_agent %in% c("DHA", "MeHg"),
"To child",
"To eater"
)
)
return(out)
},
unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d; Fish: g /d"
)
## Background-exposure to vitamin D and omega-3
addexposure <- Ovariable(
"addexposure",
ddata = "Op_en7748", # [[Benefit-risk assessment of Baltic herring and salmon intake]]
subset = "Background exposure",
unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d"
)
# Should the background be specific for gender and country? At the moment it is.
expo_bg <- Ovariable(
"expo_bg",
dependencies = data.frame(Name="addexposure","info"),
formula = function(...) {
out <- addexposure
# Empty values ("") in indices must be replaced by NA so that Ops works correctly.
levels(out$Gender)[levels(out$Gender) == ""] <- NA
levels(out$Country)[levels(out$Country) == ""] <- NA
levels(out$Exposure_agent)[levels(out$Exposure_agent) == ""] <- NA
out@output <- fillna(out@output, c("Country", "Gender", "Exposure_agent"))
temp1 <- out[out$Exposure_agent %in% c("PCDDF","PCB") , ]
temp1 <- oapply(temp1, cols = "Exposure_agent", FUN = sum)
temp1$Exposure_agent <- "TEQ"
temp2 <- out[out$Exposure_agent %in% c("EPA", "DHA") , ]
temp2 <- oapply(temp2, cols = "Exposure_agent", FUN = sum)
temp2$Exposure_agent <- "Omega3"
out <- combine(out, temp1, temp2)
out <- unkeep(out * info, prevresults = TRUE, sources = TRUE)
return(out)
},
unit = "PCDDF, PCB, TEQ: pg /d; Vitamin D, MeHg: µg /d; DHA, EPA, Omega3: mg /d"
)
# Stores non-marginal columns for further use.
info <- Ovariable(
"info",
dependencies = data.frame(Name = c("jsp")),
formula = function(...) {
out <- jsp
out$Group <- factor(
paste(out$Gender, out$Ages),
levels = c("Female 18-45", "Male 18-45", "Female >45", "Male >45")
)
out$Country <- factor(out$Country, ordered=FALSE)
out <- unique(out@output[c("Iter","Country","Group","Gender","Row")])
out$Result <- 1
return(out)
}
)
} # END IF
############################### Code from Goherr assessment ends
expo_dir <- Ovariable(
"expo_dir",
dependencies = data.frame(Name=c("conc", "amount")),
formula = function(...) {
conc$Fish[conc$Fish %in% c("Perch","Pike-perch","Eel","")] <- "Average fish"
conc <- oapply(conc, cols="", FUN=mean)
out <- conc * amount
# colnames(out@output)[colnames(out@output)=="Nutrient"] <- "Exposure_agent"
return(out)
},
unit="ng/d"
)
expo_dir <- EvalOutput(expo_dir)
#View(expo_dir@output)
ggplot(oapply(expo_dir, cols=c("Iter"),FUN=mean)@output,
aes(x=Age, weight=expo_dirResult,fill=Fish))+geom_bar()+
facet_wrap(~Compound, scales="free_y")+
labs(title="Eri yhdisteiden saanti kalasta")
ggsave("Yhdisteiden saanti kalasta Suomessa.svg")## Saving 7 x 5 in imageexposure <- Ovariable(
"exposure",
dependencies = data.frame(
Name = c(
"expo_dir", # direct exposure, i.e. the person eats or breaths the exposure agent themself
"expo_indir", # indirect exposure, i.e. the person (typically fetus or infant) is exposed via someone else (mother)
"mc2d" # 2D Monte Carlo function
),
Ident = c(
NA,
"Op_en7797/expo_indir2", # [[Infant's dioxin exposure]] # expo_indir
"Op_en7805/mc2d") # [[Two-dimensional Monte Carlo]]
),
formula = function(...) {
out <- OpasnetUtils::combine(expo_dir, expo_indir)
out <- unkeep(out, "Source.1", sources=TRUE)
out <- mc2d(out)
out$Exposure[is.na(out$Exposure)] <- "Direct"
return(out)
},
unit = "PCDDF, PCB, TEQ: (To eater: pg /day; to child: pg /g fat); Vitamin D, MeHg: µg /day; DHA, EPA, Omega3: mg /day"
)
exposure <- Ovariable("exposure", data=data.frame(Result=1))
if(FALSE) {
exposure <- EvalOutput(exposure)
View(exposure@output)
ggplot(exposure@output, aes(x=Age, weight=exposureResult, fill=Fish))+geom_bar()+
facet_grid(Compound~Exposure, scales="free_y")+
labs(
title="Exposure to compounds",
y="(omega: mg/d; vit D: ug/d, PFAS: ng/d)"
)
} # END IFobjects.latest("Op_en2261",code_name="BoDattr2") # [[Health impact assessment]]## Loading objects:
## BoDattrtryCatch(BoDattr <- EvalOutput(BoDattr, verbose=TRUE))## Evaluating BoDattr ...
##
## - BoD fetched successfully!
##
## - PAF fetched successfully!
## - Evaluating BoD ...
## - - Evaluating incidence ...
##
## done(0 secs)!
## - - Checking incidence marginals ... Response, Age, incidenceSource recognized as marginal(s).
## - - Processing incidence decisions ... done!
## - - Evaluating case_burden ...
##
## done(0 secs)!
## - - Checking case_burden marginals ... Response, case_burdenSource recognized as marginal(s).
## - - Processing case_burden marginal collapses ... done!
## - - Evaluating population ...
##
## done(0.01 secs)!
## - - Checking population marginals ... Gender, Age, populationSource recognized as marginal(s).
##
## - done(0.23 secs)!
## - Checking BoD marginals ... Response, Age, incidenceSource, Adjust, Gender, populationSource, BoDSource recognized as marginal(s).
## - Processing BoD inputs ... done!
## - Processing BoD marginal collapses ...## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying BoD, found NAs in indices: Adjust, InpBoDSource. They were
## automatically filled using fillna, which may result in a multiplied population.
## Please check your ovariable before using oapply.## done!
## - Evaluating PAF ...
##
## - - dose fetched successfully!
##
## - - ERF fetched successfully!
##
## - - frexposed fetched successfully!
##
## - - P_illness fetched successfully!
##
## - - sumExposcen fetched successfully!
##
## - - mc2d fetched successfully!
## - - Evaluating dose ...
##
## - - - BW fetched successfully!
## - - - Evaluating exposure ...
##
## done(0 secs)!
## - - - Checking exposure marginals ... exposureSource recognized as marginal(s).
## - - - Processing exposure marginal collapses ... done!
## - - - Evaluating BW ...
##
## done(0 secs)!
## - - - Checking BW marginals ... BWSource recognized as marginal(s).
##
## -- done(23.24 secs)!
## - - Checking dose marginals ... Scaling, exposureSource, BWSource, doseSource recognized as marginal(s).
## - - Processing dose marginal collapses ... done!
## - - Evaluating ERF ...
##
## - - - ERF_env fetched successfully!
##
## - - - ERF_omega3 fetched successfully!
##
## - - - ERF_mehg fetched successfully!
##
## - - - ERF_diox fetched successfully!
##
## - - - ERF_vit fetched successfully!
##
## - - - ERF_micr fetched successfully!
##
## - - - ERF_pfas fetched successfully!
## - - - Evaluating ERF_env ...
##
## done(0.02 secs)!
## - - - Checking ERF_env marginals ... Exposure_agent, Response, Subgroup, Exposure, ER_function, Scaling, Exposure_unit, Observation, ERF_envSource recognized as marginal(s).
## - - - Evaluating ERF_omega3 ...
##
## done(0 secs)!
## - - - Checking ERF_omega3 marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_omega3Source recognized as marginal(s).
## - - - Evaluating ERF_mehg ...
##
## done(0 secs)!
## - - - Checking ERF_mehg marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_mehgSource recognized as marginal(s).
## - - - Evaluating ERF_diox ...
##
## done(0 secs)!
## - - - Checking ERF_diox marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_dioxSource recognized as marginal(s).
## - - - Evaluating ERF_vit ...
##
## done(0 secs)!
## - - - Checking ERF_vit marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_vitSource recognized as marginal(s).
## - - - Evaluating ERF_micr ...
##
## done(0 secs)!
## - - - Checking ERF_micr marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_micrSource recognized as marginal(s).
## - - - Evaluating ERF_pfas ...
##
## done(0 secs)!
## - - - Checking ERF_pfas marginals ... Exposure_agent, Response, Exposure, Exposure_unit, ER_function, Scaling, Observation, ERF_pfasSource recognized as marginal(s).
## - - - Evaluating ERFchoice ...
##
## done(0 secs)!
## - - - Checking ERFchoice marginals ... Exposure_agent, Response, Scaling, Exposure, ER_function, ERFchoiceSource recognized as marginal(s).
##
## -- done(2.84 mins)!
## - - Checking ERF marginals ... Exposure_agent, Response, Exposure, ER_function, Scaling, Observation, ERFSource recognized as marginal(s).
## - - Processing ERF marginal collapses ... done!
## - - Evaluating RR ...
## - - - Processing dose marginal collapses ... done!
## - - - Processing ERF marginal collapses ... done!
##
## -- done(0.13 secs)!
## - - Checking RR marginals ... Exposure_agent, Response, ER_function, Scaling, ERFSource, doseSource, Age, RRSource recognized as marginal(s).
## - - Evaluating frexposed ...
##
## done(0 secs)!
## - - Checking frexposed marginals ... frexposedSource recognized as marginal(s).
## - - Evaluating P_illness ...
##
## done(0 secs)!
## - - Checking P_illness marginals ... Response, Illness, Age, P_illnessSource recognized as marginal(s).
##
## - done(5.61 mins)!
## - Checking PAF marginals ... Exposure_agent, Response, ER_function, Scaling, ERFSource, doseSource, frexposedSource, Age, incidenceSource, Adjust, RRSource, PAFSource recognized as marginal(s).
## - Processing PAF marginal collapses ...## Warning in oapply(variable, FUN = fun[[i]], cols = cols[[i]], na.rm = TRUE):
## While oapplying PAF, found NAs in indices: Adjust. They were automatically
## filled using fillna, which may result in a multiplied population. Please check
## your ovariable before using oapply.## done!
##
## done(6.42 mins)!
## Checking BoDattr marginals ... Response, Age, Gender, Adjust, InpBoDSource, Exposure_agent, PAFSource, BoDattrSource recognized as marginal(s).oprint(summary(amount,"mean"))## Kala Scenario Age Fish mean
## 1 Kaupallinen BAU Female 18-45 Average fish 0.7187139
## 2 Muu tuonti BAU Female 18-45 Average fish 6.2887467
## 3 Vapaa-ajan BAU Female 18-45 Average fish 2.2235211
## 4 Kaupallinen BAU Female 45+ Average fish 1.7967848
## 5 Muu tuonti BAU Female 45+ Average fish 15.7218667
## 6 Vapaa-ajan BAU Female 45+ Average fish 5.5588029
## 7 Kaupallinen BAU Non Female 18-45 Average fish 1.4374278
## 8 Muu tuonti BAU Non Female 18-45 Average fish 12.5774934
## 9 Vapaa-ajan BAU Non Female 18-45 Average fish 4.4470423
## 10 Kaupallinen BAU Non Female 45+ Average fish 2.3957130
## 11 Muu tuonti BAU Non Female 45+ Average fish 20.9624889
## 12 Vapaa-ajan BAU Non Female 45+ Average fish 7.4117372
## 13 Silakka BAU Female 18-45 Herring 0.3593570
## 14 Silakka BAU Female 45+ Herring 0.8983924
## 15 Silakka BAU Non Female 18-45 Herring 0.7187139
## 16 Silakka BAU Non Female 45+ Herring 1.1978565
## 17 Kirjolohi BAU Female 18-45 Rainbow trout 1.5048072
## 18 Tuontikirjolohi BAU Female 18-45 Rainbow trout 1.0780709
## 19 Kirjolohi BAU Female 45+ Rainbow trout 3.7620181
## 20 Tuontikirjolohi BAU Female 45+ Rainbow trout 2.6951771
## 21 Kirjolohi BAU Non Female 18-45 Rainbow trout 3.0096145
## 22 Tuontikirjolohi BAU Non Female 18-45 Rainbow trout 2.1561417
## 23 Kirjolohi BAU Non Female 45+ Rainbow trout 5.0160241
## 24 Tuontikirjolohi BAU Non Female 45+ Rainbow trout 3.5935695
## 25 Tuontilohi BAU Female 18-45 Salmon 5.3005151
## 26 Tuontilohi BAU Female 45+ Salmon 13.2512876
## 27 Tuontilohi BAU Non Female 18-45 Salmon 10.6010301
## 28 Tuontilohi BAU Non Female 45+ Salmon 17.6683835
## 29 Kasvatettu BAU Female 18-45 Whitefish 0.1347589
## 30 Kasvatettu BAU Female 45+ Whitefish 0.3368971
## 31 Kasvatettu BAU Non Female 18-45 Whitefish 0.2695177
## 32 Kasvatettu BAU Non Female 45+ Whitefish 0.4491962oprint(summary(BoD,"mean"))## Response Age Gender Adjust
## 1 Loss in child's IQ points 0 - 4 Female BAU
## 2 Sperm concentration 0 - 4 Female BAU
## 3 Yes or no dental defect 0 - 4 Female BAU
## 4 All-cause mortality 0 - 4 Female BAU
## 5 Cancer morbidity 0 - 4 Female BAU
## 6 Depression 0 - 4 Female BAU
## 7 CHD2 mortality 0 - 4 Female BAU
## 8 Immunosuppression 0 - 4 Female BAU
## 9 Dioxin recommendation tolerable daily intake Undefined Female BAU
## 10 Dioxin recommendation tolerable daily intake 2018 Undefined Female BAU
## 11 PFAS TWI Undefined Female BAU
## 12 Vitamin D recommendation Undefined Female BAU
## 13 All-cause mortality 5 - 9 Female BAU
## 14 Cancer morbidity 5 - 9 Female BAU
## 15 Depression 5 - 9 Female BAU
## 16 CHD2 mortality 5 - 9 Female BAU
## 17 Immunosuppression 5 - 9 Female BAU
## 18 All-cause mortality 10 - 14 Female BAU
## 19 Cancer morbidity 10 - 14 Female BAU
## 20 Depression 10 - 14 Female BAU
## 21 CHD2 mortality 10 - 14 Female BAU
## 22 All-cause mortality 15 - 19 Female BAU
## 23 Cancer morbidity 15 - 19 Female BAU
## 24 Depression 15 - 19 Female BAU
## 25 CHD2 mortality 15 - 19 Female BAU
## 26 Breast cancer 15 - 19 Female BAU
## 27 All-cause mortality 20 - 24 Female BAU
## 28 Cancer morbidity 20 - 24 Female BAU
## 29 Depression 20 - 24 Female BAU
## 30 CHD2 mortality 20 - 24 Female BAU
## 31 Breast cancer 20 - 24 Female BAU
## 32 All-cause mortality 25 - 29 Female BAU
## 33 Cancer morbidity 25 - 29 Female BAU
## 34 Depression 25 - 29 Female BAU
## 35 CHD2 mortality 25 - 29 Female BAU
## 36 Breast cancer 25 - 29 Female BAU
## 37 All-cause mortality 30 - 34 Female BAU
## 38 Cancer morbidity 30 - 34 Female BAU
## 39 Depression 30 - 34 Female BAU
## 40 CHD2 mortality 30 - 34 Female BAU
## 41 Breast cancer 30 - 34 Female BAU
## 42 All-cause mortality 35 - 39 Female BAU
## 43 Cancer morbidity 35 - 39 Female BAU
## 44 Depression 35 - 39 Female BAU
## 45 CHD2 mortality 35 - 39 Female BAU
## 46 Breast cancer 35 - 39 Female BAU
## 47 All-cause mortality 40 - 44 Female BAU
## 48 Cancer morbidity 40 - 44 Female BAU
## 49 Depression 40 - 44 Female BAU
## 50 CHD2 mortality 40 - 44 Female BAU
## 51 Breast cancer 40 - 44 Female BAU
## 52 All-cause mortality 45 - 49 Female BAU
## 53 Cancer morbidity 45 - 49 Female BAU
## 54 Depression 45 - 49 Female BAU
## 55 CHD2 mortality 45 - 49 Female BAU
## 56 Breast cancer 45 - 49 Female BAU
## 57 All-cause mortality 50 - 54 Female BAU
## 58 Cancer morbidity 50 - 54 Female BAU
## 59 Depression 50 - 54 Female BAU
## 60 CHD2 mortality 50 - 54 Female BAU
## 61 Breast cancer 50 - 54 Female BAU
## 62 All-cause mortality 55 - 59 Female BAU
## 63 Cancer morbidity 55 - 59 Female BAU
## 64 Depression 55 - 59 Female BAU
## 65 CHD2 mortality 55 - 59 Female BAU
## 66 Breast cancer 55 - 59 Female BAU
## 67 All-cause mortality 60 - 64 Female BAU
## 68 Cancer morbidity 60 - 64 Female BAU
## 69 Depression 60 - 64 Female BAU
## 70 CHD2 mortality 60 - 64 Female BAU
## 71 Breast cancer 60 - 64 Female BAU
## 72 All-cause mortality 65 - 69 Female BAU
## 73 Cancer morbidity 65 - 69 Female BAU
## 74 Depression 65 - 69 Female BAU
## 75 CHD2 mortality 65 - 69 Female BAU
## 76 Breast cancer 65 - 69 Female BAU
## 77 All-cause mortality 70 - 74 Female BAU
## 78 Cancer morbidity 70 - 74 Female BAU
## 79 Depression 70 - 74 Female BAU
## 80 CHD2 mortality 70 - 74 Female BAU
## 81 Breast cancer 70 - 74 Female BAU
## 82 All-cause mortality 75 - 79 Female BAU
## 83 Cancer morbidity 75 - 79 Female BAU
## 84 Depression 75 - 79 Female BAU
## 85 CHD2 mortality 75 - 79 Female BAU
## 86 Breast cancer 75 - 79 Female BAU
## 87 All-cause mortality 80 - 84 Female BAU
## 88 Cancer morbidity 80 - 84 Female BAU
## 89 Depression 80 - 84 Female BAU
## 90 CHD2 mortality 80 - 84 Female BAU
## 91 Breast cancer 80 - 84 Female BAU
## 92 All-cause mortality 85 - 89 Female BAU
## 93 Cancer morbidity 85 - 89 Female BAU
## 94 Depression 85 - 89 Female BAU
## 95 CHD2 mortality 85 - 89 Female BAU
## 96 Breast cancer 85 - 89 Female BAU
## 97 All-cause mortality 90 - 94 Female BAU
## 98 Cancer morbidity 90 - 94 Female BAU
## 99 Depression 90 - 94 Female BAU
## 100 CHD2 mortality 90 - 94 Female BAU
## 101 Breast cancer 90 - 94 Female BAU
## 102 Loss in child's IQ points 0 - 4 Male BAU
## 103 Sperm concentration 0 - 4 Male BAU
## 104 Yes or no dental defect 0 - 4 Male BAU
## 105 All-cause mortality 0 - 4 Male BAU
## 106 Cancer morbidity 0 - 4 Male BAU
## 107 Depression 0 - 4 Male BAU
## 108 CHD2 mortality 0 - 4 Male BAU
## 109 Immunosuppression 0 - 4 Male BAU
## 110 Dioxin recommendation tolerable daily intake Undefined Male BAU
## 111 Dioxin recommendation tolerable daily intake 2018 Undefined Male BAU
## 112 PFAS TWI Undefined Male BAU
## 113 Vitamin D recommendation Undefined Male BAU
## 114 All-cause mortality 5 - 9 Male BAU
## 115 Cancer morbidity 5 - 9 Male BAU
## 116 Depression 5 - 9 Male BAU
## 117 CHD2 mortality 5 - 9 Male BAU
## 118 Immunosuppression 5 - 9 Male BAU
## 119 All-cause mortality 10 - 14 Male BAU
## 120 Cancer morbidity 10 - 14 Male BAU
## 121 Depression 10 - 14 Male BAU
## 122 CHD2 mortality 10 - 14 Male BAU
## 123 All-cause mortality 15 - 19 Male BAU
## 124 Cancer morbidity 15 - 19 Male BAU
## 125 Depression 15 - 19 Male BAU
## 126 CHD2 mortality 15 - 19 Male BAU
## 127 Breast cancer 15 - 19 Male BAU
## 128 All-cause mortality 20 - 24 Male BAU
## 129 Cancer morbidity 20 - 24 Male BAU
## 130 Depression 20 - 24 Male BAU
## 131 CHD2 mortality 20 - 24 Male BAU
## 132 Breast cancer 20 - 24 Male BAU
## 133 All-cause mortality 25 - 29 Male BAU
## 134 Cancer morbidity 25 - 29 Male BAU
## 135 Depression 25 - 29 Male BAU
## 136 CHD2 mortality 25 - 29 Male BAU
## 137 Breast cancer 25 - 29 Male BAU
## 138 All-cause mortality 30 - 34 Male BAU
## 139 Cancer morbidity 30 - 34 Male BAU
## 140 Depression 30 - 34 Male BAU
## 141 CHD2 mortality 30 - 34 Male BAU
## 142 Breast cancer 30 - 34 Male BAU
## 143 All-cause mortality 35 - 39 Male BAU
## 144 Cancer morbidity 35 - 39 Male BAU
## 145 Depression 35 - 39 Male BAU
## 146 CHD2 mortality 35 - 39 Male BAU
## 147 Breast cancer 35 - 39 Male BAU
## 148 All-cause mortality 40 - 44 Male BAU
## 149 Cancer morbidity 40 - 44 Male BAU
## 150 Depression 40 - 44 Male BAU
## 151 CHD2 mortality 40 - 44 Male BAU
## 152 Breast cancer 40 - 44 Male BAU
## 153 All-cause mortality 45 - 49 Male BAU
## 154 Cancer morbidity 45 - 49 Male BAU
## 155 Depression 45 - 49 Male BAU
## 156 CHD2 mortality 45 - 49 Male BAU
## 157 Breast cancer 45 - 49 Male BAU
## 158 All-cause mortality 50 - 54 Male BAU
## 159 Cancer morbidity 50 - 54 Male BAU
## 160 Depression 50 - 54 Male BAU
## 161 CHD2 mortality 50 - 54 Male BAU
## 162 Breast cancer 50 - 54 Male BAU
## 163 All-cause mortality 55 - 59 Male BAU
## 164 Cancer morbidity 55 - 59 Male BAU
## 165 Depression 55 - 59 Male BAU
## 166 CHD2 mortality 55 - 59 Male BAU
## 167 Breast cancer 55 - 59 Male BAU
## 168 All-cause mortality 60 - 64 Male BAU
## 169 Cancer morbidity 60 - 64 Male BAU
## 170 Depression 60 - 64 Male BAU
## 171 CHD2 mortality 60 - 64 Male BAU
## 172 Breast cancer 60 - 64 Male BAU
## 173 All-cause mortality 65 - 69 Male BAU
## 174 Cancer morbidity 65 - 69 Male BAU
## 175 Depression 65 - 69 Male BAU
## 176 CHD2 mortality 65 - 69 Male BAU
## 177 Breast cancer 65 - 69 Male BAU
## 178 All-cause mortality 70 - 74 Male BAU
## 179 Cancer morbidity 70 - 74 Male BAU
## 180 Depression 70 - 74 Male BAU
## 181 CHD2 mortality 70 - 74 Male BAU
## 182 Breast cancer 70 - 74 Male BAU
## 183 All-cause mortality 75 - 79 Male BAU
## 184 Cancer morbidity 75 - 79 Male BAU
## 185 Depression 75 - 79 Male BAU
## 186 CHD2 mortality 75 - 79 Male BAU
## 187 Breast cancer 75 - 79 Male BAU
## 188 All-cause mortality 80 - 84 Male BAU
## 189 Cancer morbidity 80 - 84 Male BAU
## 190 Depression 80 - 84 Male BAU
## 191 CHD2 mortality 80 - 84 Male BAU
## 192 Breast cancer 80 - 84 Male BAU
## 193 All-cause mortality 85 - 89 Male BAU
## 194 Cancer morbidity 85 - 89 Male BAU
## 195 Depression 85 - 89 Male BAU
## 196 CHD2 mortality 85 - 89 Male BAU
## 197 Breast cancer 85 - 89 Male BAU
## 198 All-cause mortality 90 - 94 Male BAU
## 199 Cancer morbidity 90 - 94 Male BAU
## 200 Depression 90 - 94 Male BAU
## 201 CHD2 mortality 90 - 94 Male BAU
## 202 Breast cancer 90 - 94 Male BAU
## mean
## 1 16395.2448
## 2 4376.4000
## 3 336.1075
## 4 4367.2700
## 5 279.4400
## 6 0.4400
## 7 51.4100
## 8 272.0400
## 9 309.2078
## 10 899.5137
## 11 2810.9804
## 12 618.4157
## 13 909.3800
## 14 352.6300
## 15 87.5700
## 16 57.2800
## 17 248.0400
## 18 1012.2200
## 19 357.1200
## 20 680.2900
## 21 87.0900
## 22 1945.6100
## 23 386.2000
## 24 1657.4000
## 25 153.9800
## 26 3.0000
## 27 2684.3600
## 28 524.2800
## 29 2295.7100
## 30 228.8100
## 31 14.4000
## 32 2980.0200
## 33 728.7600
## 34 2185.6700
## 35 342.4600
## 36 72.4600
## 37 3569.3800
## 38 1173.6800
## 39 1876.2400
## 40 456.4700
## 41 289.2600
## 42 4372.6700
## 43 1750.8000
## 44 2009.3100
## 45 655.6500
## 46 525.1700
## 47 5831.2700
## 48 2660.2800
## 49 1980.0500
## 50 1017.7100
## 51 863.5600
## 52 8481.7500
## 53 4204.3800
## 54 1805.0000
## 55 1511.8900
## 56 1403.4500
## 57 13751.6900
## 58 7197.9400
## 59 2050.3500
## 60 2418.4000
## 61 2079.3300
## 62 19024.8900
## 63 10372.2500
## 64 2139.7100
## 65 3830.7900
## 66 2667.9900
## 67 25603.4700
## 68 14317.8700
## 69 2050.2600
## 70 6174.0400
## 71 3008.1400
## 72 34094.4500
## 73 18551.3000
## 74 1910.2400
## 75 10009.4600
## 76 3280.9700
## 77 44484.9600
## 78 21707.2100
## 79 1738.8100
## 80 16202.4200
## 81 3328.9100
## 82 41490.0000
## 83 15942.7400
## 84 992.1300
## 85 18996.6800
## 86 2448.5100
## 87 51345.4900
## 88 13735.8800
## 89 754.1900
## 90 28299.7900
## 91 1889.1800
## 92 49589.0500
## 93 8476.7900
## 94 508.9600
## 95 30471.0100
## 96 1151.1600
## 97 36317.0200
## 98 4083.6700
## 99 282.5900
## 100 23285.7800
## 101 589.9300
## 102 17161.5101
## 103 4580.9400
## 104 351.8162
## 105 5367.6600
## 106 339.6000
## 107 0.3900
## 108 55.8100
## 109 319.9300
## 110 301.6056
## 111 877.3982
## 112 2741.8694
## 113 603.2113
## 114 952.8700
## 115 338.4100
## 116 65.4000
## 117 40.2800
## 118 275.0300
## 119 1158.8700
## 120 337.0300
## 121 431.4800
## 122 63.5400
## 123 3986.2800
## 124 443.7500
## 125 984.2500
## 126 136.7100
## 127 1.1800
## 128 7865.3300
## 129 614.7700
## 130 1386.3200
## 131 279.3900
## 132 1.1700
## 133 9547.0600
## 134 862.1500
## 135 1417.2600
## 136 532.8300
## 137 1.1800
## 138 9851.2400
## 139 1122.2900
## 140 1303.8000
## 141 816.3700
## 142 1.0600
## 143 12178.6200
## 144 1607.4500
## 145 1434.1400
## 146 1680.8700
## 147 2.0300
## 148 14632.7600
## 149 2244.8500
## 150 1420.5100
## 151 2937.4200
## 152 3.1400
## 153 18900.8100
## 154 3494.3800
## 155 1263.3400
## 156 4722.7300
## 157 6.4600
## 158 28892.0400
## 159 6777.8600
## 160 1387.5600
## 161 8534.1800
## 162 11.3200
## 163 40698.9300
## 164 12102.4100
## 165 1394.6200
## 166 13664.1400
## 167 15.6400
## 168 52798.2800
## 169 18345.9500
## 170 1303.2200
## 171 19981.9000
## 172 18.2900
## 173 64496.5600
## 174 25248.0000
## 175 1212.9100
## 176 26192.0400
## 177 17.6900
## 178 75114.4400
## 179 29396.5100
## 180 1113.6600
## 181 32896.1500
## 182 27.1600
## 183 57367.0300
## 184 20087.7200
## 185 607.2700
## 186 27830.2900
## 187 15.5100
## 188 52592.3400
## 189 15565.9500
## 190 408.4000
## 191 27498.8800
## 192 12.5800
## 193 35941.6400
## 194 8349.3200
## 195 217.1900
## 196 20009.0600
## 197 5.2200
## 198 17196.2600
## 199 2990.6800
## 200 89.6400
## 201 9975.4500
## 202 2.4300oprint(summary(BoDattr,"mean"))## Response Age Gender Adjust
## 1 Loss in child's IQ points 0 - 4 Female BAU
## 2 Loss in child's IQ points 0 - 4 Male BAU
## 3 Loss in child's IQ points 0 - 4 Female BAU
## 4 Loss in child's IQ points 0 - 4 Male BAU
## 5 Sperm concentration 0 - 4 Female BAU
## 6 Yes or no dental defect 0 - 4 Female BAU
## 7 Dioxin recommendation tolerable daily intake Undefined Female BAU
## 8 Dioxin recommendation tolerable daily intake 2018 Undefined Female BAU
## 9 Sperm concentration 0 - 4 Male BAU
## 10 Yes or no dental defect 0 - 4 Male BAU
## 11 Dioxin recommendation tolerable daily intake Undefined Male BAU
## 12 Dioxin recommendation tolerable daily intake 2018 Undefined Male BAU
## 13 PFAS TWI Undefined Female BAU
## 14 PFAS TWI Undefined Male BAU
## 15 Vitamin D recommendation Undefined Female BAU
## 16 Vitamin D recommendation Undefined Male BAU
## 17 All-cause mortality 0 - 4 Female BAU
## 18 Depression 0 - 4 Female BAU
## 19 All-cause mortality 5 - 9 Female BAU
## 20 Depression 5 - 9 Female BAU
## 21 All-cause mortality 10 - 14 Female BAU
## 22 Depression 10 - 14 Female BAU
## 23 All-cause mortality 15 - 19 Female BAU
## 24 Depression 15 - 19 Female BAU
## 25 All-cause mortality 20 - 24 Female BAU
## 26 Depression 20 - 24 Female BAU
## 27 All-cause mortality 25 - 29 Female BAU
## 28 Depression 25 - 29 Female BAU
## 29 All-cause mortality 30 - 34 Female BAU
## 30 Depression 30 - 34 Female BAU
## 31 All-cause mortality 35 - 39 Female BAU
## 32 Depression 35 - 39 Female BAU
## 33 All-cause mortality 40 - 44 Female BAU
## 34 Depression 40 - 44 Female BAU
## 35 All-cause mortality 45 - 49 Female BAU
## 36 Depression 45 - 49 Female BAU
## 37 All-cause mortality 50 - 54 Female BAU
## 38 Depression 50 - 54 Female BAU
## 39 All-cause mortality 55 - 59 Female BAU
## 40 Depression 55 - 59 Female BAU
## 41 All-cause mortality 60 - 64 Female BAU
## 42 Depression 60 - 64 Female BAU
## 43 All-cause mortality 65 - 69 Female BAU
## 44 Depression 65 - 69 Female BAU
## 45 All-cause mortality 70 - 74 Female BAU
## 46 Depression 70 - 74 Female BAU
## 47 All-cause mortality 75 - 79 Female BAU
## 48 Depression 75 - 79 Female BAU
## 49 All-cause mortality 80 - 84 Female BAU
## 50 Depression 80 - 84 Female BAU
## 51 All-cause mortality 0 - 4 Male BAU
## 52 Depression 0 - 4 Male BAU
## 53 All-cause mortality 5 - 9 Male BAU
## 54 Depression 5 - 9 Male BAU
## 55 All-cause mortality 10 - 14 Male BAU
## 56 Depression 10 - 14 Male BAU
## 57 All-cause mortality 15 - 19 Male BAU
## 58 Depression 15 - 19 Male BAU
## 59 All-cause mortality 20 - 24 Male BAU
## 60 Depression 20 - 24 Male BAU
## 61 All-cause mortality 25 - 29 Male BAU
## 62 Depression 25 - 29 Male BAU
## 63 All-cause mortality 30 - 34 Male BAU
## 64 Depression 30 - 34 Male BAU
## 65 All-cause mortality 35 - 39 Male BAU
## 66 Depression 35 - 39 Male BAU
## 67 All-cause mortality 40 - 44 Male BAU
## 68 Depression 40 - 44 Male BAU
## 69 All-cause mortality 45 - 49 Male BAU
## 70 Depression 45 - 49 Male BAU
## 71 All-cause mortality 50 - 54 Male BAU
## 72 Depression 50 - 54 Male BAU
## 73 All-cause mortality 55 - 59 Male BAU
## 74 Depression 55 - 59 Male BAU
## 75 All-cause mortality 60 - 64 Male BAU
## 76 Depression 60 - 64 Male BAU
## 77 All-cause mortality 65 - 69 Male BAU
## 78 Depression 65 - 69 Male BAU
## 79 All-cause mortality 70 - 74 Male BAU
## 80 Depression 70 - 74 Male BAU
## 81 All-cause mortality 75 - 79 Male BAU
## 82 Depression 75 - 79 Male BAU
## 83 All-cause mortality 80 - 84 Male BAU
## 84 Depression 80 - 84 Male BAU
## 85 CHD2 mortality 0 - 4 Female BAU
## 86 CHD2 mortality 5 - 9 Female BAU
## 87 CHD2 mortality 10 - 14 Female BAU
## 88 CHD2 mortality 15 - 19 Female BAU
## 89 Breast cancer 15 - 19 Female BAU
## 90 CHD2 mortality 20 - 24 Female BAU
## 91 Breast cancer 20 - 24 Female BAU
## 92 CHD2 mortality 25 - 29 Female BAU
## 93 Breast cancer 25 - 29 Female BAU
## 94 CHD2 mortality 30 - 34 Female BAU
## 95 Breast cancer 30 - 34 Female BAU
## 96 CHD2 mortality 35 - 39 Female BAU
## 97 Breast cancer 35 - 39 Female BAU
## 98 CHD2 mortality 40 - 44 Female BAU
## 99 Breast cancer 40 - 44 Female BAU
## 100 CHD2 mortality 45 - 49 Female BAU
## 101 Breast cancer 45 - 49 Female BAU
## 102 CHD2 mortality 50 - 54 Female BAU
## 103 Breast cancer 50 - 54 Female BAU
## 104 CHD2 mortality 55 - 59 Female BAU
## 105 Breast cancer 55 - 59 Female BAU
## 106 CHD2 mortality 60 - 64 Female BAU
## 107 Breast cancer 60 - 64 Female BAU
## 108 CHD2 mortality 65 - 69 Female BAU
## 109 Breast cancer 65 - 69 Female BAU
## 110 CHD2 mortality 70 - 74 Female BAU
## 111 Breast cancer 70 - 74 Female BAU
## 112 CHD2 mortality 75 - 79 Female BAU
## 113 Breast cancer 75 - 79 Female BAU
## 114 CHD2 mortality 80 - 84 Female BAU
## 115 Breast cancer 80 - 84 Female BAU
## 116 CHD2 mortality 0 - 4 Male BAU
## 117 CHD2 mortality 5 - 9 Male BAU
## 118 CHD2 mortality 10 - 14 Male BAU
## 119 CHD2 mortality 15 - 19 Male BAU
## 120 Breast cancer 15 - 19 Male BAU
## 121 CHD2 mortality 20 - 24 Male BAU
## 122 Breast cancer 20 - 24 Male BAU
## 123 CHD2 mortality 25 - 29 Male BAU
## 124 Breast cancer 25 - 29 Male BAU
## 125 CHD2 mortality 30 - 34 Male BAU
## 126 Breast cancer 30 - 34 Male BAU
## 127 CHD2 mortality 35 - 39 Male BAU
## 128 Breast cancer 35 - 39 Male BAU
## 129 CHD2 mortality 40 - 44 Male BAU
## 130 Breast cancer 40 - 44 Male BAU
## 131 CHD2 mortality 45 - 49 Male BAU
## 132 Breast cancer 45 - 49 Male BAU
## 133 CHD2 mortality 50 - 54 Male BAU
## 134 Breast cancer 50 - 54 Male BAU
## 135 CHD2 mortality 55 - 59 Male BAU
## 136 Breast cancer 55 - 59 Male BAU
## 137 CHD2 mortality 60 - 64 Male BAU
## 138 Breast cancer 60 - 64 Male BAU
## 139 CHD2 mortality 65 - 69 Male BAU
## 140 Breast cancer 65 - 69 Male BAU
## 141 CHD2 mortality 70 - 74 Male BAU
## 142 Breast cancer 70 - 74 Male BAU
## 143 CHD2 mortality 75 - 79 Male BAU
## 144 Breast cancer 75 - 79 Male BAU
## 145 CHD2 mortality 80 - 84 Male BAU
## 146 Breast cancer 80 - 84 Male BAU
## Exposure_agent mean
## 1 DHA -1.788072e+01
## 2 DHA -1.871641e+01
## 3 MeHg 1.925616e+03
## 4 MeHg 2.015614e+03
## 5 TEQ 1.875600e+01
## 6 TEQ 1.043562e+01
## 7 TEQ -2.501773e+03
## 8 TEQ -1.911467e+03
## 9 TEQ 1.963260e+01
## 10 TEQ 1.092335e+01
## 11 TEQ -2.440264e+03
## 12 TEQ -1.864471e+03
## 13 PFAS 0.000000e+00
## 14 PFAS 0.000000e+00
## 15 Vitamin D 6.184157e+02
## 16 Vitamin D 6.032113e+02
## 17 Fish -9.294861e+00
## 18 Fish -2.336224e-03
## 19 Fish -1.935433e+00
## 20 Fish -4.649617e-01
## 21 Fish -2.154308e+00
## 22 Fish -3.612068e+00
## 23 Fish -4.140842e+00
## 24 Fish -8.800131e+00
## 25 Fish -5.713123e+00
## 26 Fish -1.218930e+01
## 27 Fish -6.342377e+00
## 28 Fish -1.160503e+01
## 29 Fish -7.596711e+00
## 30 Fish -9.962084e+00
## 31 Fish -9.306354e+00
## 32 Fish -1.066863e+01
## 33 Fish -1.241069e+01
## 34 Fish -1.051327e+01
## 35 Fish -1.805171e+01
## 36 Fish -9.583828e+00
## 37 Fish -2.926772e+01
## 38 Fish -1.088654e+01
## 39 Fish -4.049067e+01
## 40 Fish -1.136100e+01
## 41 Fish -5.449187e+01
## 42 Fish -1.088606e+01
## 43 Fish -7.256322e+01
## 44 Fish -1.014261e+01
## 45 Fish -9.467734e+01
## 46 Fish -9.232386e+00
## 47 Fish -8.830317e+01
## 48 Fish -5.267813e+00
## 49 Fish -1.092786e+02
## 50 Fish -4.004447e+00
## 51 Fish -1.142399e+01
## 52 Fish -2.070744e-03
## 53 Fish -2.027993e+00
## 54 Fish -3.472478e-01
## 55 Fish -2.466423e+00
## 56 Fish -2.290986e+00
## 57 Fish -8.484000e+00
## 58 Fish -5.225974e+00
## 59 Fish -1.673978e+01
## 60 Fish -7.360805e+00
## 61 Fish -2.031901e+01
## 62 Fish -7.525084e+00
## 63 Fish -2.096639e+01
## 64 Fish -6.922656e+00
## 65 Fish -2.591976e+01
## 66 Fish -7.614710e+00
## 67 Fish -3.114290e+01
## 68 Fish -7.542340e+00
## 69 Fish -4.022659e+01
## 70 Fish -6.707830e+00
## 71 Fish -6.149093e+01
## 72 Fish -7.367389e+00
## 73 Fish -8.661953e+01
## 74 Fish -7.404874e+00
## 75 Fish -1.123706e+02
## 76 Fish -6.919577e+00
## 77 Fish -1.372680e+02
## 78 Fish -6.440067e+00
## 79 Fish -1.598661e+02
## 80 Fish -5.913089e+00
## 81 Fish -1.220942e+02
## 82 Fish -3.224361e+00
## 83 Fish -1.119323e+02
## 84 Fish -2.168441e+00
## 85 Omega3 -1.820771e-01
## 86 Omega3 -2.028667e-01
## 87 Omega3 -3.084437e-01
## 88 Omega3 -5.453458e-01
## 89 Omega3 -1.538400e-03
## 90 Omega3 -8.103687e-01
## 91 Omega3 -7.384320e-03
## 92 Omega3 -1.212879e+00
## 93 Omega3 -3.715749e-02
## 94 Omega3 -1.616665e+00
## 95 Omega3 -1.483325e-01
## 96 Omega3 -2.322094e+00
## 97 Omega3 -2.693072e-01
## 98 Omega3 -3.604390e+00
## 99 Omega3 -4.428336e-01
## 100 Omega3 -5.354610e+00
## 101 Omega3 -7.196892e-01
## 102 Omega3 -8.565167e+00
## 103 Omega3 -1.066280e+00
## 104 Omega3 -1.356738e+01
## 105 Omega3 -1.368145e+00
## 106 Omega3 -2.186639e+01
## 107 Omega3 -1.542574e+00
## 108 Omega3 -3.545017e+01
## 109 Omega3 -1.682481e+00
## 110 Omega3 -5.738357e+01
## 111 Omega3 -1.707065e+00
## 112 Omega3 -6.727991e+01
## 113 Omega3 -1.255596e+00
## 114 Omega3 -1.002284e+02
## 115 Omega3 -9.687715e-01
## 116 Omega3 -1.976604e-01
## 117 Omega3 -1.426583e-01
## 118 Omega3 -2.250375e-01
## 119 Omega3 -4.841812e-01
## 120 Omega3 -6.051040e-04
## 121 Omega3 -9.895062e-01
## 122 Omega3 -5.999760e-04
## 123 Omega3 -1.887106e+00
## 124 Omega3 -6.051040e-04
## 125 Omega3 -2.891310e+00
## 126 Omega3 -5.435680e-04
## 127 Omega3 -5.953081e+00
## 128 Omega3 -1.040984e-03
## 129 Omega3 -1.040336e+01
## 130 Omega3 -1.610192e-03
## 131 Omega3 -1.672634e+01
## 132 Omega3 -3.312688e-03
## 133 Omega3 -3.022522e+01
## 134 Omega3 -5.804896e-03
## 135 Omega3 -4.839383e+01
## 136 Omega3 -8.020192e-03
## 137 Omega3 -7.076923e+01
## 138 Omega3 -9.379112e-03
## 139 Omega3 -9.276347e+01
## 140 Omega3 -9.071432e-03
## 141 Omega3 -1.165072e+02
## 142 Omega3 -1.392765e-02
## 143 Omega3 -9.856561e+01
## 144 Omega3 -7.953528e-03
## 145 Omega3 -9.739187e+01
## 146 Omega3 -6.451024e-03oprint(summary(case_burden,"mean"))## Response mean
## 1 Dioxin recommendation tolerable daily intake 0.001004988
## 2 Dioxin recommendation tolerable daily intake 2018 0.001004988
## 3 Loss in child's IQ points 0.110000000
## 4 PFAS TWI 0.001004988
## 5 Sperm concentration 2.500000000
## 6 Vitamin D recommendation 0.001004988
## 7 Yes or no dental defect 0.060000000oprint(summary(conc,"mean"))## Fish Compound mean
## 1 Average fish ALA 0.690000
## 2 Bream ALA 0.220000
## 3 Herring ALA 1.740000
## 4 Pike ALA 0.080000
## 5 Rainbow trout ALA 4.810000
## 6 Roach ALA 0.100000
## 7 Salmon ALA 7.960000
## 8 Vendace ALA 1.350000
## 9 Whitefish ALA 2.220000
## 10 Average fish DHA 2.540000
## 11 Bream DHA 2.730000
## 12 Herring DHA 5.860000
## 13 Pike DHA 0.300000
## 14 Rainbow trout DHA 7.570000
## 15 Roach DHA 2.870000
## 16 Salmon DHA 6.690000
## 17 Vendace DHA 3.000000
## 18 Whitefish DHA 3.940000
## 19 Average fish Fish 1.000000
## 20 Bream Fish 1.000000
## 21 Herring Fish 1.000000
## 22 Pike Fish 1.000000
## 23 Rainbow trout Fish 1.000000
## 24 Roach Fish 1.000000
## 25 Salmon Fish 1.000000
## 26 Vendace Fish 1.000000
## 27 Whitefish Fish 1.000000
## 28 Average fish Omega3 7.000000
## 29 Bream Omega3 6.000000
## 30 Herring Omega3 24.000000
## 31 Pike Omega3 0.500000
## 32 Rainbow trout Omega3 18.000000
## 33 Roach Omega3 5.000000
## 34 Salmon Omega3 23.000000
## 35 Vendace Omega3 10.000000
## 36 Whitefish Omega3 10.000000
## 37 Bream PFAS 4.950000
## 38 Eel PFAS 7.895000
## 39 Herring PFAS 1.655000
## 40 Perch PFAS 8.793125
## 41 Pike-perch PFAS 2.595000
## 42 Average fish Vitamin D 0.105000
## 43 Bream Vitamin D 0.140000
## 44 Herring Vitamin D 0.156000
## 45 Pike Vitamin D 0.021000
## 46 Rainbow trout Vitamin D 0.051000
## 47 Roach Vitamin D 0.100000
## 48 Salmon Vitamin D 0.067000
## 49 Vendace Vitamin D 0.094000
## 50 Whitefish Vitamin D 0.144000oprint(summary(dose,"mean"))## Scaling mean
## 1 BW 0.01428571
## 2 Log10 0.00000000
## 3 None 1.00000000oprint(summary(ERF,"mean"))## Exposure_agent Response
## 1 Fish All-cause mortality
## 2 Omega3 Breast cancer
## 3 Fish Depression
## 4 DHA Loss in child's IQ points
## 5 TEQ Sperm concentration
## 6 TEQ Yes or no dental defect
## 7 Omega3 CHD2 mortality
## 8 Vitamin D Vitamin D recommendation
## 9 MeHg Loss in child's IQ points
## 10 PFAS Immunosuppression
## 11 PFAS PFAS TWI
## 12 TEQ Cancer morbidity
## 13 TEQ Dioxin recommendation tolerable daily intake
## 14 TEQ Dioxin recommendation tolerable daily intake 2018
## 15 Fish All-cause mortality
## 16 Omega3 Breast cancer
## 17 Fish Depression
## 18 DHA Loss in child's IQ points
## 19 TEQ Sperm concentration
## 20 TEQ Yes or no dental defect
## 21 Omega3 CHD2 mortality
## 22 Vitamin D Vitamin D recommendation
## 23 MeHg Loss in child's IQ points
## 24 PFAS Immunosuppression
## 25 PFAS PFAS TWI
## 26 TEQ Cancer morbidity
## 27 TEQ Dioxin recommendation tolerable daily intake
## 28 TEQ Dioxin recommendation tolerable daily intake 2018
## ER_function Scaling Observation mean
## 1 RR None ERF 0.997871700
## 2 RR None ERF 0.999487200
## 3 RR None ERF 0.994690400
## 4 ERS None ERF -0.001300000
## 5 ERS None ERF 0.000060000
## 6 ERS None ERF 0.001390971
## 7 Relative Hill None ERF -0.170000000
## 8 Step None ERF 100.000000000
## 9 ERS BW ERF 9.800000000
## 10 ERS BW ERF 0.022700000
## 11 TWI BW ERF 4.400000000
## 12 CSF BW ERF 0.000500000
## 13 TDI BW ERF 2.000000000
## 14 TDI BW ERF 0.288900000
## 15 RR None Threshold 0.000000000
## 16 RR None Threshold 0.000000000
## 17 RR None Threshold 0.000000000
## 18 ERS None Threshold 0.000000000
## 19 ERS None Threshold 0.000000000
## 20 ERS None Threshold 0.000000000
## 21 Relative Hill None Threshold 47.000000000
## 22 Step None Threshold 10.000000000
## 23 ERS BW Threshold 0.000000000
## 24 ERS BW Threshold 0.000000000
## 25 TWI BW Threshold 0.000000000
## 26 CSF BW Threshold 0.000000000
## 27 TDI BW Threshold 0.000000000
## 28 TDI BW Threshold 0.000000000oprint(summary(expo_dir,"mean"))## Fish Compound Kala Scenario Age
## 1 Whitefish ALA Kasvatettu BAU Female 18-45
## 2 Whitefish DHA Kasvatettu BAU Female 18-45
## 3 Whitefish Fish Kasvatettu BAU Female 18-45
## 4 Whitefish Omega3 Kasvatettu BAU Female 18-45
## 5 Whitefish Vitamin D Kasvatettu BAU Female 18-45
## 6 Average fish ALA Kaupallinen BAU Female 18-45
## 7 Average fish DHA Kaupallinen BAU Female 18-45
## 8 Average fish Fish Kaupallinen BAU Female 18-45
## 9 Average fish Omega3 Kaupallinen BAU Female 18-45
## 10 Average fish PFAS Kaupallinen BAU Female 18-45
## 11 Average fish Vitamin D Kaupallinen BAU Female 18-45
## 12 Rainbow trout ALA Kirjolohi BAU Female 18-45
## 13 Rainbow trout DHA Kirjolohi BAU Female 18-45
## 14 Rainbow trout Fish Kirjolohi BAU Female 18-45
## 15 Rainbow trout Omega3 Kirjolohi BAU Female 18-45
## 16 Rainbow trout Vitamin D Kirjolohi BAU Female 18-45
## 17 Average fish ALA Muu tuonti BAU Female 18-45
## 18 Average fish DHA Muu tuonti BAU Female 18-45
## 19 Average fish Fish Muu tuonti BAU Female 18-45
## 20 Average fish Omega3 Muu tuonti BAU Female 18-45
## 21 Average fish PFAS Muu tuonti BAU Female 18-45
## 22 Average fish Vitamin D Muu tuonti BAU Female 18-45
## 23 Herring ALA Silakka BAU Female 18-45
## 24 Herring DHA Silakka BAU Female 18-45
## 25 Herring Fish Silakka BAU Female 18-45
## 26 Herring Omega3 Silakka BAU Female 18-45
## 27 Herring PFAS Silakka BAU Female 18-45
## 28 Herring Vitamin D Silakka BAU Female 18-45
## 29 Rainbow trout ALA Tuontikirjolohi BAU Female 18-45
## 30 Rainbow trout DHA Tuontikirjolohi BAU Female 18-45
## 31 Rainbow trout Fish Tuontikirjolohi BAU Female 18-45
## 32 Rainbow trout Omega3 Tuontikirjolohi BAU Female 18-45
## 33 Rainbow trout Vitamin D Tuontikirjolohi BAU Female 18-45
## 34 Salmon ALA Tuontilohi BAU Female 18-45
## 35 Salmon DHA Tuontilohi BAU Female 18-45
## 36 Salmon Fish Tuontilohi BAU Female 18-45
## 37 Salmon Omega3 Tuontilohi BAU Female 18-45
## 38 Salmon Vitamin D Tuontilohi BAU Female 18-45
## 39 Average fish ALA Vapaa-ajan BAU Female 18-45
## 40 Average fish DHA Vapaa-ajan BAU Female 18-45
## 41 Average fish Fish Vapaa-ajan BAU Female 18-45
## 42 Average fish Omega3 Vapaa-ajan BAU Female 18-45
## 43 Average fish PFAS Vapaa-ajan BAU Female 18-45
## 44 Average fish Vitamin D Vapaa-ajan BAU Female 18-45
## 45 Whitefish ALA Kasvatettu BAU Female 45+
## 46 Whitefish DHA Kasvatettu BAU Female 45+
## 47 Whitefish Fish Kasvatettu BAU Female 45+
## 48 Whitefish Omega3 Kasvatettu BAU Female 45+
## 49 Whitefish Vitamin D Kasvatettu BAU Female 45+
## 50 Average fish ALA Kaupallinen BAU Female 45+
## 51 Average fish DHA Kaupallinen BAU Female 45+
## 52 Average fish Fish Kaupallinen BAU Female 45+
## 53 Average fish Omega3 Kaupallinen BAU Female 45+
## 54 Average fish PFAS Kaupallinen BAU Female 45+
## 55 Average fish Vitamin D Kaupallinen BAU Female 45+
## 56 Rainbow trout ALA Kirjolohi BAU Female 45+
## 57 Rainbow trout DHA Kirjolohi BAU Female 45+
## 58 Rainbow trout Fish Kirjolohi BAU Female 45+
## 59 Rainbow trout Omega3 Kirjolohi BAU Female 45+
## 60 Rainbow trout Vitamin D Kirjolohi BAU Female 45+
## 61 Average fish ALA Muu tuonti BAU Female 45+
## 62 Average fish DHA Muu tuonti BAU Female 45+
## 63 Average fish Fish Muu tuonti BAU Female 45+
## 64 Average fish Omega3 Muu tuonti BAU Female 45+
## 65 Average fish PFAS Muu tuonti BAU Female 45+
## 66 Average fish Vitamin D Muu tuonti BAU Female 45+
## 67 Herring ALA Silakka BAU Female 45+
## 68 Herring DHA Silakka BAU Female 45+
## 69 Herring Fish Silakka BAU Female 45+
## 70 Herring Omega3 Silakka BAU Female 45+
## 71 Herring PFAS Silakka BAU Female 45+
## 72 Herring Vitamin D Silakka BAU Female 45+
## 73 Rainbow trout ALA Tuontikirjolohi BAU Female 45+
## 74 Rainbow trout DHA Tuontikirjolohi BAU Female 45+
## 75 Rainbow trout Fish Tuontikirjolohi BAU Female 45+
## 76 Rainbow trout Omega3 Tuontikirjolohi BAU Female 45+
## 77 Rainbow trout Vitamin D Tuontikirjolohi BAU Female 45+
## 78 Salmon ALA Tuontilohi BAU Female 45+
## 79 Salmon DHA Tuontilohi BAU Female 45+
## 80 Salmon Fish Tuontilohi BAU Female 45+
## 81 Salmon Omega3 Tuontilohi BAU Female 45+
## 82 Salmon Vitamin D Tuontilohi BAU Female 45+
## 83 Average fish ALA Vapaa-ajan BAU Female 45+
## 84 Average fish DHA Vapaa-ajan BAU Female 45+
## 85 Average fish Fish Vapaa-ajan BAU Female 45+
## 86 Average fish Omega3 Vapaa-ajan BAU Female 45+
## 87 Average fish PFAS Vapaa-ajan BAU Female 45+
## 88 Average fish Vitamin D Vapaa-ajan BAU Female 45+
## 89 Whitefish ALA Kasvatettu BAU Non Female 18-45
## 90 Whitefish DHA Kasvatettu BAU Non Female 18-45
## 91 Whitefish Fish Kasvatettu BAU Non Female 18-45
## 92 Whitefish Omega3 Kasvatettu BAU Non Female 18-45
## 93 Whitefish Vitamin D Kasvatettu BAU Non Female 18-45
## 94 Average fish ALA Kaupallinen BAU Non Female 18-45
## 95 Average fish DHA Kaupallinen BAU Non Female 18-45
## 96 Average fish Fish Kaupallinen BAU Non Female 18-45
## 97 Average fish Omega3 Kaupallinen BAU Non Female 18-45
## 98 Average fish PFAS Kaupallinen BAU Non Female 18-45
## 99 Average fish Vitamin D Kaupallinen BAU Non Female 18-45
## 100 Rainbow trout ALA Kirjolohi BAU Non Female 18-45
## 101 Rainbow trout DHA Kirjolohi BAU Non Female 18-45
## 102 Rainbow trout Fish Kirjolohi BAU Non Female 18-45
## 103 Rainbow trout Omega3 Kirjolohi BAU Non Female 18-45
## 104 Rainbow trout Vitamin D Kirjolohi BAU Non Female 18-45
## 105 Average fish ALA Muu tuonti BAU Non Female 18-45
## 106 Average fish DHA Muu tuonti BAU Non Female 18-45
## 107 Average fish Fish Muu tuonti BAU Non Female 18-45
## 108 Average fish Omega3 Muu tuonti BAU Non Female 18-45
## 109 Average fish PFAS Muu tuonti BAU Non Female 18-45
## 110 Average fish Vitamin D Muu tuonti BAU Non Female 18-45
## 111 Herring ALA Silakka BAU Non Female 18-45
## 112 Herring DHA Silakka BAU Non Female 18-45
## 113 Herring Fish Silakka BAU Non Female 18-45
## 114 Herring Omega3 Silakka BAU Non Female 18-45
## 115 Herring PFAS Silakka BAU Non Female 18-45
## 116 Herring Vitamin D Silakka BAU Non Female 18-45
## 117 Rainbow trout ALA Tuontikirjolohi BAU Non Female 18-45
## 118 Rainbow trout DHA Tuontikirjolohi BAU Non Female 18-45
## 119 Rainbow trout Fish Tuontikirjolohi BAU Non Female 18-45
## 120 Rainbow trout Omega3 Tuontikirjolohi BAU Non Female 18-45
## 121 Rainbow trout Vitamin D Tuontikirjolohi BAU Non Female 18-45
## 122 Salmon ALA Tuontilohi BAU Non Female 18-45
## 123 Salmon DHA Tuontilohi BAU Non Female 18-45
## 124 Salmon Fish Tuontilohi BAU Non Female 18-45
## 125 Salmon Omega3 Tuontilohi BAU Non Female 18-45
## 126 Salmon Vitamin D Tuontilohi BAU Non Female 18-45
## 127 Average fish ALA Vapaa-ajan BAU Non Female 18-45
## 128 Average fish DHA Vapaa-ajan BAU Non Female 18-45
## 129 Average fish Fish Vapaa-ajan BAU Non Female 18-45
## 130 Average fish Omega3 Vapaa-ajan BAU Non Female 18-45
## 131 Average fish PFAS Vapaa-ajan BAU Non Female 18-45
## 132 Average fish Vitamin D Vapaa-ajan BAU Non Female 18-45
## 133 Whitefish ALA Kasvatettu BAU Non Female 45+
## 134 Whitefish DHA Kasvatettu BAU Non Female 45+
## 135 Whitefish Fish Kasvatettu BAU Non Female 45+
## 136 Whitefish Omega3 Kasvatettu BAU Non Female 45+
## 137 Whitefish Vitamin D Kasvatettu BAU Non Female 45+
## 138 Average fish ALA Kaupallinen BAU Non Female 45+
## 139 Average fish DHA Kaupallinen BAU Non Female 45+
## 140 Average fish Fish Kaupallinen BAU Non Female 45+
## 141 Average fish Omega3 Kaupallinen BAU Non Female 45+
## 142 Average fish PFAS Kaupallinen BAU Non Female 45+
## 143 Average fish Vitamin D Kaupallinen BAU Non Female 45+
## 144 Rainbow trout ALA Kirjolohi BAU Non Female 45+
## 145 Rainbow trout DHA Kirjolohi BAU Non Female 45+
## 146 Rainbow trout Fish Kirjolohi BAU Non Female 45+
## 147 Rainbow trout Omega3 Kirjolohi BAU Non Female 45+
## 148 Rainbow trout Vitamin D Kirjolohi BAU Non Female 45+
## 149 Average fish ALA Muu tuonti BAU Non Female 45+
## 150 Average fish DHA Muu tuonti BAU Non Female 45+
## 151 Average fish Fish Muu tuonti BAU Non Female 45+
## 152 Average fish Omega3 Muu tuonti BAU Non Female 45+
## 153 Average fish PFAS Muu tuonti BAU Non Female 45+
## 154 Average fish Vitamin D Muu tuonti BAU Non Female 45+
## 155 Herring ALA Silakka BAU Non Female 45+
## 156 Herring DHA Silakka BAU Non Female 45+
## 157 Herring Fish Silakka BAU Non Female 45+
## 158 Herring Omega3 Silakka BAU Non Female 45+
## 159 Herring PFAS Silakka BAU Non Female 45+
## 160 Herring Vitamin D Silakka BAU Non Female 45+
## 161 Rainbow trout ALA Tuontikirjolohi BAU Non Female 45+
## 162 Rainbow trout DHA Tuontikirjolohi BAU Non Female 45+
## 163 Rainbow trout Fish Tuontikirjolohi BAU Non Female 45+
## 164 Rainbow trout Omega3 Tuontikirjolohi BAU Non Female 45+
## 165 Rainbow trout Vitamin D Tuontikirjolohi BAU Non Female 45+
## 166 Salmon ALA Tuontilohi BAU Non Female 45+
## 167 Salmon DHA Tuontilohi BAU Non Female 45+
## 168 Salmon Fish Tuontilohi BAU Non Female 45+
## 169 Salmon Omega3 Tuontilohi BAU Non Female 45+
## 170 Salmon Vitamin D Tuontilohi BAU Non Female 45+
## 171 Average fish ALA Vapaa-ajan BAU Non Female 45+
## 172 Average fish DHA Vapaa-ajan BAU Non Female 45+
## 173 Average fish Fish Vapaa-ajan BAU Non Female 45+
## 174 Average fish Omega3 Vapaa-ajan BAU Non Female 45+
## 175 Average fish PFAS Vapaa-ajan BAU Non Female 45+
## 176 Average fish Vitamin D Vapaa-ajan BAU Non Female 45+
## mean
## 1 0.29916466
## 2 0.53094990
## 3 0.13475886
## 4 1.34758857
## 5 0.01940528
## 6 0.49591260
## 7 1.82553332
## 8 0.71871391
## 9 5.03099734
## 10 4.61968337
## 11 0.07546496
## 12 7.23812283
## 13 11.39139082
## 14 1.50480724
## 15 27.08653035
## 16 0.07674517
## 17 4.33923521
## 18 15.97341657
## 19 6.28874668
## 20 44.02122677
## 21 40.42222945
## 22 0.66031840
## 23 0.62528110
## 24 2.10583175
## 25 0.35935695
## 26 8.62456688
## 27 0.59473576
## 28 0.05605968
## 29 5.18552083
## 30 8.16099641
## 31 1.07807086
## 32 19.40527547
## 33 0.05498161
## 34 42.19209987
## 35 35.46044575
## 36 5.30051506
## 37 121.91184637
## 38 0.35513451
## 39 1.53422959
## 40 5.64774372
## 41 2.22352115
## 42 15.56464804
## 43 14.29214541
## 44 0.23346972
## 45 0.74791166
## 46 1.32737475
## 47 0.33689714
## 48 3.36897144
## 49 0.04851319
## 50 1.23978149
## 51 4.56383331
## 52 1.79678477
## 53 12.57749336
## 54 11.54920841
## 55 0.18866240
## 56 18.09530708
## 57 28.47847705
## 58 3.76201810
## 59 67.71632587
## 60 0.19186292
## 61 10.84808802
## 62 39.93354142
## 63 15.72186670
## 64 110.05306692
## 65 101.05557362
## 66 1.65079600
## 67 1.56320275
## 68 5.26457936
## 69 0.89839238
## 70 21.56141719
## 71 1.48683939
## 72 0.14014921
## 73 12.96380209
## 74 20.40249102
## 75 2.69517715
## 76 48.51318868
## 77 0.13745403
## 78 105.48024969
## 79 88.65111437
## 80 13.25128765
## 81 304.77961593
## 82 0.88783627
## 83 3.83557398
## 84 14.11935929
## 85 5.55880287
## 86 38.91162009
## 87 35.73036353
## 88 0.58367430
## 89 0.59832933
## 90 1.06189980
## 91 0.26951771
## 92 2.69517715
## 93 0.03881055
## 94 0.99182519
## 95 3.65106664
## 96 1.43742781
## 97 10.06199469
## 98 9.23936673
## 99 0.15092992
## 100 14.47624566
## 101 22.78278164
## 102 3.00961448
## 103 54.17306069
## 104 0.15349034
## 105 8.67847042
## 106 31.94683314
## 107 12.57749336
## 108 88.04245353
## 109 80.84445889
## 110 1.32063680
## 111 1.25056220
## 112 4.21166349
## 113 0.71871391
## 114 17.24913375
## 115 1.18947152
## 116 0.11211937
## 117 10.37104167
## 118 16.32199281
## 119 2.15614172
## 120 38.81055094
## 121 0.10996323
## 122 84.38419975
## 123 70.92089150
## 124 10.60103012
## 125 243.82369274
## 126 0.71026902
## 127 3.06845918
## 128 11.29548743
## 129 4.44704230
## 130 31.12929607
## 131 28.58429082
## 132 0.46693944
## 133 0.99721555
## 134 1.76983299
## 135 0.44919619
## 136 4.49196191
## 137 0.06468425
## 138 1.65304198
## 139 6.08511107
## 140 2.39571302
## 141 16.76999115
## 142 15.39894455
## 143 0.25154987
## 144 24.12707611
## 145 37.97130273
## 146 5.01602414
## 147 90.28843449
## 148 0.25581723
## 149 14.46411737
## 150 53.24472190
## 151 20.96248894
## 152 146.73742255
## 153 134.74076482
## 154 2.20106134
## 155 2.08427033
## 156 7.01943915
## 157 1.19785651
## 158 28.74855626
## 159 1.98245253
## 160 0.18686562
## 161 17.28506945
## 162 27.20332136
## 163 3.59356953
## 164 64.68425157
## 165 0.18327205
## 166 140.64033291
## 167 118.20148583
## 168 17.66838353
## 169 406.37282123
## 170 1.18378170
## 171 5.11409864
## 172 18.82581239
## 173 7.41173716
## 174 51.88216012
## 175 47.64048471
## 176 0.77823240#oprint(summary(exposure,"mean"))
oprint(summary(fish_proportion,"mean"))## Age mean
## 1 Female 18-45 0.4528302
## 2 Female 45+ 1.1320755
## 3 Non Female 18-45 0.9056604
## 4 Non Female 45+ 1.5094340oprint(summary(incidence,"mean"))## Response Age Adjust mean
## 1 Loss in child's IQ points 0 - 4 BAU 1.1920
## 2 Sperm concentration 0 - 4 BAU 0.0140
## 3 Yes or no dental defect 0 - 4 BAU 0.0448
## 4 Dioxin recommendation tolerable daily intake Undefined BAU 0.1100
## 5 Dioxin recommendation tolerable daily intake 2018 Undefined BAU 0.3200
## 6 PFAS TWI Undefined BAU 1.0000
## 7 Vitamin D recommendation Undefined BAU 0.2200oprint(summary(PAF,"mean"))## Exposure_agent Response Age
## 1 DHA Loss in child's IQ points 0 - 4
## 2 MeHg Loss in child's IQ points 0 - 4
## 3 TEQ Sperm concentration 0 - 4
## 4 TEQ Yes or no dental defect 0 - 4
## 5 Fish All-cause mortality 0 - 4
## 6 Omega3 Breast cancer 0 - 4
## 7 Fish Depression 0 - 4
## 8 Omega3 CHD2 mortality 0 - 4
## 9 Fish All-cause mortality 10 - 14
## 10 Omega3 Breast cancer 10 - 14
## 11 Fish Depression 10 - 14
## 12 Omega3 CHD2 mortality 10 - 14
## 13 Fish All-cause mortality 15 - 19
## 14 Omega3 Breast cancer 15 - 19
## 15 Fish Depression 15 - 19
## 16 Omega3 CHD2 mortality 15 - 19
## 17 Fish All-cause mortality 20 - 24
## 18 Omega3 Breast cancer 20 - 24
## 19 Fish Depression 20 - 24
## 20 Omega3 CHD2 mortality 20 - 24
## 21 Fish All-cause mortality 25 - 29
## 22 Omega3 Breast cancer 25 - 29
## 23 Fish Depression 25 - 29
## 24 Omega3 CHD2 mortality 25 - 29
## 25 Fish All-cause mortality 30 - 34
## 26 Omega3 Breast cancer 30 - 34
## 27 Fish Depression 30 - 34
## 28 Omega3 CHD2 mortality 30 - 34
## 29 Fish All-cause mortality 35 - 39
## 30 Omega3 Breast cancer 35 - 39
## 31 Fish Depression 35 - 39
## 32 Omega3 CHD2 mortality 35 - 39
## 33 Fish All-cause mortality 40 - 44
## 34 Omega3 Breast cancer 40 - 44
## 35 Fish Depression 40 - 44
## 36 Omega3 CHD2 mortality 40 - 44
## 37 Fish All-cause mortality 45 - 49
## 38 Omega3 Breast cancer 45 - 49
## 39 Fish Depression 45 - 49
## 40 Omega3 CHD2 mortality 45 - 49
## 41 Fish All-cause mortality 5 - 9
## 42 Omega3 Breast cancer 5 - 9
## 43 Fish Depression 5 - 9
## 44 Omega3 CHD2 mortality 5 - 9
## 45 Fish All-cause mortality 50 - 54
## 46 Omega3 Breast cancer 50 - 54
## 47 Fish Depression 50 - 54
## 48 Omega3 CHD2 mortality 50 - 54
## 49 Fish All-cause mortality 55 - 59
## 50 Omega3 Breast cancer 55 - 59
## 51 Fish Depression 55 - 59
## 52 Omega3 CHD2 mortality 55 - 59
## 53 Fish All-cause mortality 60 - 64
## 54 Omega3 Breast cancer 60 - 64
## 55 Fish Depression 60 - 64
## 56 Omega3 CHD2 mortality 60 - 64
## 57 Fish All-cause mortality 65 - 69
## 58 Omega3 Breast cancer 65 - 69
## 59 Fish Depression 65 - 69
## 60 Omega3 CHD2 mortality 65 - 69
## 61 Fish All-cause mortality 70 - 74
## 62 Omega3 Breast cancer 70 - 74
## 63 Fish Depression 70 - 74
## 64 Omega3 CHD2 mortality 70 - 74
## 65 Fish All-cause mortality 75 - 79
## 66 Omega3 Breast cancer 75 - 79
## 67 Fish Depression 75 - 79
## 68 Omega3 CHD2 mortality 75 - 79
## 69 Fish All-cause mortality 80 - 84
## 70 Omega3 Breast cancer 80 - 84
## 71 Fish Depression 80 - 84
## 72 Omega3 CHD2 mortality 80 - 84
## 73 Fish All-cause mortality 85+
## 74 Omega3 Breast cancer 85+
## 75 Fish Depression 85+
## 76 Omega3 CHD2 mortality 85+
## 77 TEQ Dioxin recommendation tolerable daily intake Undefined
## 78 TEQ Dioxin recommendation tolerable daily intake 2018 Undefined
## 79 Vitamin D Vitamin D recommendation Undefined
## 80 PFAS PFAS TWI Undefined
## Adjust mean
## 1 BAU -0.001090604
## 2 BAU 0.117449664
## 3 BAU 0.004285714
## 4 BAU 0.031048457
## 5 BAU -0.002128300
## 6 BAU -0.000512800
## 7 BAU -0.005309600
## 8 BAU -0.003541667
## 9 BAU -0.002128300
## 10 BAU -0.000512800
## 11 BAU -0.005309600
## 12 BAU -0.003541667
## 13 BAU -0.002128300
## 14 BAU -0.000512800
## 15 BAU -0.005309600
## 16 BAU -0.003541667
## 17 BAU -0.002128300
## 18 BAU -0.000512800
## 19 BAU -0.005309600
## 20 BAU -0.003541667
## 21 BAU -0.002128300
## 22 BAU -0.000512800
## 23 BAU -0.005309600
## 24 BAU -0.003541667
## 25 BAU -0.002128300
## 26 BAU -0.000512800
## 27 BAU -0.005309600
## 28 BAU -0.003541667
## 29 BAU -0.002128300
## 30 BAU -0.000512800
## 31 BAU -0.005309600
## 32 BAU -0.003541667
## 33 BAU -0.002128300
## 34 BAU -0.000512800
## 35 BAU -0.005309600
## 36 BAU -0.003541667
## 37 BAU -0.002128300
## 38 BAU -0.000512800
## 39 BAU -0.005309600
## 40 BAU -0.003541667
## 41 BAU -0.002128300
## 42 BAU -0.000512800
## 43 BAU -0.005309600
## 44 BAU -0.003541667
## 45 BAU -0.002128300
## 46 BAU -0.000512800
## 47 BAU -0.005309600
## 48 BAU -0.003541667
## 49 BAU -0.002128300
## 50 BAU -0.000512800
## 51 BAU -0.005309600
## 52 BAU -0.003541667
## 53 BAU -0.002128300
## 54 BAU -0.000512800
## 55 BAU -0.005309600
## 56 BAU -0.003541667
## 57 BAU -0.002128300
## 58 BAU -0.000512800
## 59 BAU -0.005309600
## 60 BAU -0.003541667
## 61 BAU -0.002128300
## 62 BAU -0.000512800
## 63 BAU -0.005309600
## 64 BAU -0.003541667
## 65 BAU -0.002128300
## 66 BAU -0.000512800
## 67 BAU -0.005309600
## 68 BAU -0.003541667
## 69 BAU -0.002128300
## 70 BAU -0.000512800
## 71 BAU -0.005309600
## 72 BAU -0.003541667
## 73 BAU -0.002128300
## 74 BAU -0.000512800
## 75 BAU -0.005309600
## 76 BAU -0.003541667
## 77 BAU -8.090909091
## 78 BAU -2.125000000
## 79 BAU 1.000000000
## 80 BAU 0.000000000oprint(summary(population,"mean"))## Gender Age mean
## 1 Female 0 - 4 125040
## 2 Male 0 - 4 130884
## 3 Female 10 - 14 151113
## 4 Male 10 - 14 157712
## 5 Female 15 - 19 144441
## 6 Male 15 - 19 152230
## 7 Female 20 - 24 152265
## 8 Male 20 - 24 161679
## 9 Female 25 - 29 172593
## 10 Male 25 - 29 183092
## 11 Female 30 - 34 169653
## 12 Male 30 - 34 181115
## 13 Female 35 - 39 174660
## 14 Male 35 - 39 186122
## 15 Female 40 - 44 168547
## 16 Male 40 - 44 177928
## 17 Female 45 - 49 154391
## 18 Male 45 - 49 159982
## 19 Female 5 - 9 149633
## 20 Male 5 - 9 156654
## 21 Female 50 - 54 176612
## 22 Male 50 - 54 179182
## 23 Female 55 - 59 185152
## 24 Male 55 - 59 183719
## 25 Female 60 - 64 183336
## 26 Male 60 - 64 176283
## 27 Female 65 - 69 185685
## 28 Male 65 - 69 171275
## 29 Female 70 - 74 186034
## 30 Male 70 - 74 163697
## 31 Female 75 - 79 118190
## 32 Male 75 - 79 93987
## 33 Female 80 - 84 96256
## 34 Male 80 - 84 65140
## 35 Female 85+ 103429
## 36 Male 85+ 47581
## 37 Female Undefined 2797030
## 38 Male Undefined 2728262oprint(summary(RR,"mean"))## Exposure_agent Response ER_function Scaling Age mean
## 1 Fish All-cause mortality RR None 0 - 4 0.9978717
## 2 Omega3 Breast cancer RR None 0 - 4 0.9994872
## 3 Fish Depression RR None 0 - 4 0.9946904
## 4 Omega3 CHD2 mortality Relative Hill None 0 - 4 0.9964583
## 5 Fish All-cause mortality RR None 10 - 14 0.9978717
## 6 Omega3 Breast cancer RR None 10 - 14 0.9994872
## 7 Fish Depression RR None 10 - 14 0.9946904
## 8 Omega3 CHD2 mortality Relative Hill None 10 - 14 0.9964583
## 9 Fish All-cause mortality RR None 15 - 19 0.9978717
## 10 Omega3 Breast cancer RR None 15 - 19 0.9994872
## 11 Fish Depression RR None 15 - 19 0.9946904
## 12 Omega3 CHD2 mortality Relative Hill None 15 - 19 0.9964583
## 13 Fish All-cause mortality RR None 20 - 24 0.9978717
## 14 Omega3 Breast cancer RR None 20 - 24 0.9994872
## 15 Fish Depression RR None 20 - 24 0.9946904
## 16 Omega3 CHD2 mortality Relative Hill None 20 - 24 0.9964583
## 17 Fish All-cause mortality RR None 25 - 29 0.9978717
## 18 Omega3 Breast cancer RR None 25 - 29 0.9994872
## 19 Fish Depression RR None 25 - 29 0.9946904
## 20 Omega3 CHD2 mortality Relative Hill None 25 - 29 0.9964583
## 21 Fish All-cause mortality RR None 30 - 34 0.9978717
## 22 Omega3 Breast cancer RR None 30 - 34 0.9994872
## 23 Fish Depression RR None 30 - 34 0.9946904
## 24 Omega3 CHD2 mortality Relative Hill None 30 - 34 0.9964583
## 25 Fish All-cause mortality RR None 35 - 39 0.9978717
## 26 Omega3 Breast cancer RR None 35 - 39 0.9994872
## 27 Fish Depression RR None 35 - 39 0.9946904
## 28 Omega3 CHD2 mortality Relative Hill None 35 - 39 0.9964583
## 29 Fish All-cause mortality RR None 40 - 44 0.9978717
## 30 Omega3 Breast cancer RR None 40 - 44 0.9994872
## 31 Fish Depression RR None 40 - 44 0.9946904
## 32 Omega3 CHD2 mortality Relative Hill None 40 - 44 0.9964583
## 33 Fish All-cause mortality RR None 45 - 49 0.9978717
## 34 Omega3 Breast cancer RR None 45 - 49 0.9994872
## 35 Fish Depression RR None 45 - 49 0.9946904
## 36 Omega3 CHD2 mortality Relative Hill None 45 - 49 0.9964583
## 37 Fish All-cause mortality RR None 5 - 9 0.9978717
## 38 Omega3 Breast cancer RR None 5 - 9 0.9994872
## 39 Fish Depression RR None 5 - 9 0.9946904
## 40 Omega3 CHD2 mortality Relative Hill None 5 - 9 0.9964583
## 41 Fish All-cause mortality RR None 50 - 54 0.9978717
## 42 Omega3 Breast cancer RR None 50 - 54 0.9994872
## 43 Fish Depression RR None 50 - 54 0.9946904
## 44 Omega3 CHD2 mortality Relative Hill None 50 - 54 0.9964583
## 45 Fish All-cause mortality RR None 55 - 59 0.9978717
## 46 Omega3 Breast cancer RR None 55 - 59 0.9994872
## 47 Fish Depression RR None 55 - 59 0.9946904
## 48 Omega3 CHD2 mortality Relative Hill None 55 - 59 0.9964583
## 49 Fish All-cause mortality RR None 60 - 64 0.9978717
## 50 Omega3 Breast cancer RR None 60 - 64 0.9994872
## 51 Fish Depression RR None 60 - 64 0.9946904
## 52 Omega3 CHD2 mortality Relative Hill None 60 - 64 0.9964583
## 53 Fish All-cause mortality RR None 65 - 69 0.9978717
## 54 Omega3 Breast cancer RR None 65 - 69 0.9994872
## 55 Fish Depression RR None 65 - 69 0.9946904
## 56 Omega3 CHD2 mortality Relative Hill None 65 - 69 0.9964583
## 57 Fish All-cause mortality RR None 70 - 74 0.9978717
## 58 Omega3 Breast cancer RR None 70 - 74 0.9994872
## 59 Fish Depression RR None 70 - 74 0.9946904
## 60 Omega3 CHD2 mortality Relative Hill None 70 - 74 0.9964583
## 61 Fish All-cause mortality RR None 75 - 79 0.9978717
## 62 Omega3 Breast cancer RR None 75 - 79 0.9994872
## 63 Fish Depression RR None 75 - 79 0.9946904
## 64 Omega3 CHD2 mortality Relative Hill None 75 - 79 0.9964583
## 65 Fish All-cause mortality RR None 80 - 84 0.9978717
## 66 Omega3 Breast cancer RR None 80 - 84 0.9994872
## 67 Fish Depression RR None 80 - 84 0.9946904
## 68 Omega3 CHD2 mortality Relative Hill None 80 - 84 0.9964583
## 69 Fish All-cause mortality RR None 85+ 0.9978717
## 70 Omega3 Breast cancer RR None 85+ 0.9994872
## 71 Fish Depression RR None 85+ 0.9946904
## 72 Omega3 CHD2 mortality Relative Hill None 85+ 0.9964583###################
# Graphs
trim <- function(ova) return(oapply(ova, NULL, mean, "Iter")@output)
plot_ly(trim(amount), x=~Scenario, y=~amountResult, color=~Kala, type="bar") %>%
layout(yaxis=list(title="Kalan kokonaiskulutus Suomessa (milj kg /a)"), barmode="stack")## Warning: `arrange_()` is deprecated as of dplyr 0.7.0.
## Please use `arrange()` instead.
## See vignette('programming') for more help
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.plot_ly(trim(conc_vit), x=~Nutrient, y=~conc_vitResult, color=~Kala, type="scatter", mode="markers") %>%
layout(yaxis=list(title="Concentrations of nutrients (mg or ug /g)"))## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colorstmp <- exposure / Ovariable(
output = data.frame(
Exposure_agent = c("Fish","Vitamin D", "Omega3", "ALA", "DHA", "TEQ", "PFAS"),
Result = c(1, 1, 1000, 1000, 1000, 1, 1)
),
marginal = c(TRUE, FALSE)
)
plot_ly(trim(tmp), x=~exposureSource, y=~Result, color=~Exposure_agent, text=~Exposure_agent, type="bar") %>%
layout(yaxis=list(title="Exposure to nutrients (g or ug /d)"))cat("Kalaperäisiä tautitaakkoja Suomessa\n")## Kalaperäisiä tautitaakkoja Suomessaif(openv$N>1) {
tmp <- summary(oapply(BoDattr,NULL,sum,c("Age","Gender","Response")))
tmp <- data.frame(
Altiste = tmp$Exposure_agent,
Vaikutus = tmp$Response,
Keskiarvo = as.character(signif(tmp$mean,2)),
"95 luottamusväli" = paste0(signif(tmp$Q0.025,2)," - ", signif(tmp$Q0.975,2)),
Keskihajonta = signif(tmp$sd,2)
)#[rev(match(lev, tmp$Exposure_agent)),]
oprint(tmp)
tmp <- summary(oapply(BoDattr,NULL,sum,c("Age","Gender","Exposure_agent")))
tmp <- data.frame(
Terveysvaikutus = tmp$Response,
Keskiarvo = signif(tmp$mean,2),
"95 luottamusväli" = paste0(signif(tmp$Q0.025,2)," - ", signif(tmp$Q0.975,2)),
Keskihajonta = signif(tmp$sd,2)
)
oprint(tmp)
}
ggplot(trim(BoDattr), aes(x=Exposure_agent, weight=BoDattrResult, fill=Response))+geom_bar()
ggplot(trim(BoDattr), aes(x=Response, weight=BoDattrResult, fill=Exposure_agent))+geom_bar()
plot_ly(trim(BoDattr), x=~Exposure_agent, y=~BoDattrResult, color=~Response, text=~paste(Age, Exposure_agent, sep=": "), type="bar") %>%
layout(yaxis=list(title="Disease burden (DALY /a); CHD2=coronary heart disease"), barmode="stack")## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors
## Warning in RColorBrewer::brewer.pal(N, "Set2"): n too large, allowed maximum for palette Set2 is 8
## Returning the palette you asked for with that many colors################ Insight network
gr <- scrape(type="assessment")
objects.latest("Op_en3861", "makeGraph") # [[Insight network]]## Loading objects:
## makeGraphgr <- makeGraph(gr)## Loading required package: DiagrammeR## Loading objects:
## formatted
## Loading objects:
## chooseGr#export_graph(gr, "ruori.svg")
#render_graph(gr) # Does not work: Error in generate_dot(graph) : object 'attribute' not found
##################### Diagnostics
objects.latest("Op_en6007", code_name="diagnostics")## Loading objects:
## showind
## binoptest
## showLoctable
## ovashapetestshowLoctable()showind()## subgrouping is not an ovariable.
## sumExposcen is not an ovariable.
## mc2d is not an ovariable.
## mc2dparam is not an ovariable.